Video signals are often corrupted by noise during acquisition or transmission processes. Noise is a major source of degradation in picture quality. As TV screens get ever larger, video noise has become more annoying to viewers. Therefore, there has been a need for high quality noise reduction systems to improve video quality.
It is well known that the human eye is more sensitive to noise in still background than that in moving areas where noise is masked by the human visual system. As such, conventional motion adaptive noise reduction methods attempt to reduce video noise accordingly. In such methods, a motion detector is used to detect motion for every pixel in a video frame to generate a motion signal. The motion signal indicates the moving areas and non-moving areas within a video frame. For the non-moving areas a temporal filter is applied to reduce the video noise. For the moving areas the temporal filter is switched off to avoid motion blurring, and instead a spatial filter is applied to reduce noise within each single frame. Although spatial filtering is less effective than temporal filtering for video noise reduction, residual noise left by spatial filtering in moving areas is less perceptible to the human eye.
In general there are two types of temporal filters. One is the temporal averaging filter which takes averaging of multiple successive frames, and the other is the temporal recursive filter which recursively takes averaging between the current frame and the preceding processed frame. When dealing with Gaussian type noise, which is the most common noise in video signals, the temporal averaging filter is preferred. However, the temporal averaging filter is traditionally considered as having two shortcomings. The first shortcoming is that it uses more frame memory because its performance is directly affected by the number of frames used. The second shortcoming is that because it uses multiple frames, the chance that motion occurs among those frames is increased. This makes the temporal averaging filter being switched off to avoid motion blurring, therefore leaving larger noisy areas around moving objects.
As memory costs continue to decrease and consumer demand for high picture quality video products increases, the first shortcoming of the temporal averaging filter has become less important, while the second shortcoming, which directly affects the picture quality, has become a more serious issue.
There is, therefore, a need for a system and method that provides high quality noise reduction and significantly improves video quality.